Structural Equation Modeling

In this talk I introduce the concepts and jargon of structural equation modeling (SEM) including path diagrams, latent variables, endogenous and exogenous variables, and goodness of fit. I describe the similarities and differences between Stata's -sem- and -gsem- commands. I then demonstrate how to fit many familiar models such as linear regression, multivariate regression, logistic regression, confirmatory factor analysis, and multilevel models using -sem- and -gsem-. I conclude by demonstrating how to fit structural equation models that contain both structural and measurement components.

Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health and at the New York University School of Global Public Health. In addition to working with Stata's team of software developers, he produces instructional videos for the Stata Youtube channel, writes blog entries, develops online NetCourses and gives talks about Stata at conferences and universities. Most of his current work is focused on statistical methods used by behavioral and health scientists. He has published in the areas of neurology, human and andimal genetics, alcohol and drube abuse prevention, nutrition and birth defects. Dr. Huber currently teaches survey sampling at NYU and introductory biostatistics at Texas A&M where he previously taught categorical data analysis, survey data analysis, and statistical genetics.

Host or Publisher Center for Statistical Consulting & ICTS - BERD